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https://github.com/kjappelbaum/mofdscribe
An ecosystem for digital reticular chemistry
https://github.com/kjappelbaum/mofdscribe
artificial-intelligence benchmark data-science descriptors featurization hacktoberfest machine-learning metal-organic-frameworks metrics ml mof porous-materials reticular-chemistry splitting
Last synced: 5 days ago
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An ecosystem for digital reticular chemistry
- Host: GitHub
- URL: https://github.com/kjappelbaum/mofdscribe
- Owner: kjappelbaum
- License: mit
- Created: 2022-03-18T08:13:49.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-10T16:42:21.000Z (2 months ago)
- Last Synced: 2024-09-10T18:44:39.454Z (2 months ago)
- Topics: artificial-intelligence, benchmark, data-science, descriptors, featurization, hacktoberfest, machine-learning, metal-organic-frameworks, metrics, ml, mof, porous-materials, reticular-chemistry, splitting
- Language: Python
- Homepage: https://mofdscribe.readthedocs.io/en/latest/
- Size: 11.8 MB
- Stars: 43
- Watchers: 4
- Forks: 7
- Open Issues: 121
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
Featurizing metal-organic frameworks (MOFs) made simple! This package builds on the power of [matminer](https://hackingmaterials.lbl.gov/matminer/) to make featurization of MOFs as easy as possible. Now, you can use features that are mostly used for porous materials in the same way as all other matminer featurizers.
mofdscribe additionally includes routines that help with model validation.
## 💪 Getting Started
```python
from mofdscribe.featurizers.chemistry import RACS
from pymatgen.core import Structure
structure = Structure.from_file()
featurizer = RACS()
racs_features = featurizer.featurize(structure)
```
## 🚀 Installation
While we are in the process of trying to make mofdscribe work on all operating system (we're waiting for conda recipies getting merged),
it is currently not easy on Windows (and there might be potential issues on ARM-based Macs).
For this reason, we recommend installing mofdscribe on a UNIX machine.
To install in development mode, use the following:
```bash
git clone git+https://github.com/kjappelbaum/mofdscribe.git
cd mofdscribe
pip install -e .
```
if you want to use all utilities, you can use the `all` extra: `pip install -e ".[all]"`
We depend on many other external tools. Most external tools are automatically installed if you install mofdscribe via conda:
```bash
conda install -c conda-forge mofdscribe
```
## 👐 Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See
[CONTRIBUTING.rst](https://github.com/kjappelbaum/mofdscribe/blob/master/CONTRIBUTING.rst) for more information on getting involved.
## 👋 Attribution
### ⚖️ License
The code in this package is licensed under the MIT License.
### 📖 Citation
See the [ChemRxiv preprint](https://chemrxiv.org/engage/chemrxiv/article-details/630d1f6f90802d52c16eceb2).
```
@article{Jablonka_2022,
doi = {10.26434/chemrxiv-2022-4g7rx},
url = {https://doi.org/10.26434%2Fchemrxiv-2022-4g7rx},
year = 2022,
month = {sep},
publisher = {American Chemical Society ({ACS})},
author = {Kevin Maik Jablonka and Andrew S. Rosen and Aditi S. Krishnapriyan and Berend Smit},
title = {An ecosystem for digital reticular chemistry}
}
```
### 💰 Funding
The research was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme ([grant agreement 666983, MaGic](https://cordis.europa.eu/project/id/666983)), by the [NCCR-MARVEL](https://www.nccr-marvel.ch/), funded by the Swiss National Science Foundation, and by the Swiss National Science Foundation (SNSF) under Grant 200021_172759.
### 🍪 Cookiecutter
This package was created with [@audreyfeldroy](https://github.com/audreyfeldroy)'s
[cookiecutter](https://github.com/cookiecutter/cookiecutter) package using [@cthoyt](https://github.com/cthoyt)'s
[cookiecutter-snekpack](https://github.com/cthoyt/cookiecutter-snekpack) template.
## 🛠️ For Developers
See developer instructions
The final section of the README is for if you want to get involved by making a code contribution.
### ❓ Testing
After cloning the repository and installing `tox` with `pip install tox`, the unit tests in the `tests/` folder can be
run reproducibly with:
```shell
tox
```
Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/kjappelbaum/mofdscribe/actions?query=workflow%3ATests).
### 📦 Making a Release
After installing the package in development mode and installing
`tox` with `pip install tox`, the commands for making a new release are contained within the `finish` environment
in `tox.ini`. Run the following from the shell:
```shell
tox -e finish
```
This script does the following:
1. Uses BumpVersion to switch the version number in the `setup.cfg` and
`src/mofdscribe/version.py` to not have the `-dev` suffix
2. Packages the code in both a tar archive and a wheel
3. Uploads to PyPI using `twine`. Be sure to have a `.pypirc` file configured to avoid the need for manual input at this
step
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
use `tox -e bumpversion minor` after.